A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation

Cao, Qingjie; Shi, Zaifeng; Wang, Rong; Wang, Pumeng; Yao, Suying

doi:10.1007/s11042-020-09265-y

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…Image segmentation based on entropy is very popular, and entropy can be divided into Shannon entropy [ 39 ], tsallis entropy [ 13 ], exponential entropy [ 42 , 51 ] and arimoto entropy [ 30 ], etc. According to the dimension of image histogram, it also can be classified into one-dimensional (1-D) histogram, two-dimensional (2-D) histogram [ 1 , 6 , 45 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

Threshold image segmentation based on improved sparrow search algorithm

Yuan

2022

Multimed Tools Appl

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Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

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Section: Introductionmentioning

confidence: 99%

Threshold image segmentation based on improved sparrow search algorithm

Yuan

2022

Multimed Tools Appl

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“…Finally, it is restored to the original RGB color system, and thus, the new enhanced contrast RGB image is recovered. Some techniques based on histogram equalization [24] are the following:…”

Section: Linear Nonlinearmentioning

confidence: 99%

A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images

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Trigka

2022

Sensors

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Smart devices have become an integral part of people’s lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YCbCr color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a,b that meet the user’s preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance.

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“…As a result, the enhanced image has a reduced quality. With the aid of histogram segmentation, Cao et al [4] performed histogram equalization of each sub histogram to improve the brightness of low-illumination images; the histogram segmentation alleviates the problem of excessive enhancement to a certain extent. Qadar et al [5] proposed a recursive separation weighted histogram equalization algorithm, which decomposes the histogram into several sub histograms, weighs each sub histogram, and then implement histogram equalization; however, the quality of the enhanced image often degrades, due to the loss of statistical information.…”

Section: Image Enhancementmentioning

confidence: 99%

An Image Sharpness Enhancement Algorithm Based on Green Function

Huang¹

2021

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The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.

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A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation

Cited by 8 publications

References 27 publications

Threshold image segmentation based on improved sparrow search algorithm

Threshold image segmentation based on improved sparrow search algorithm

A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images

An Image Sharpness Enhancement Algorithm Based on Green Function

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